PDeepPP is a hybrid protein language model designed to predict post-translational modification (PTM) sites, analyze biologically relevant features, and support a wide range of protein sequence analysis tasks. This repository serves as the central hub for accessing and exploring various specialized PDeepPP models, each fine-tuned for specific tasks, such as PTM site prediction, bioactivity analysis, and more.
PDeepPP integrates state-of-the-art transformer-based self-attention mechanisms with convolutional neural networks (CNNs) to capture both global and local features in protein sequences. By leveraging pretrained embeddings from ESM and incorporating modular architecture components, PDeepPP offers a robust framework for protein sequence analysis.
This repository contains links to multiple task-specific PDeepPP models. These models are pre-trained or fine-tuned on publicly available datasets and are hosted on Hugging Face for easy access.
Key Features
Flexible Architecture: Combines self-attention and convolutional operations for robust feature extraction.
Task-Specific Models: Includes pre-trained models for PTM prediction, bioactivity classification, and more.
Dataset Support: Models are validated on datasets such as PTM and BPS, ensuring performance on real-world tasks.
Extensibility: Users can fine-tune the models on custom datasets for new tasks.
